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        <span>python_Accuracy,Precison,Recall,F1 score</span>
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        <h1 id="Accuracy-Precison-Recall-F1-score"><a href="#Accuracy-Precison-Recall-F1-score" class="headerlink" title="Accuracy,Precison,Recall,F1 score"></a>Accuracy,Precison,Recall,F1 score</h1><p>主要参考这两篇博文</p>
<p><a target="_blank" rel="noopener" href="https://blog.csdn.net/u014380165/article/details/77493978">https://blog.csdn.net/u014380165/article/details/77493978</a></p>
<p><a target="_blank" rel="noopener" href="https://www.cnblogs.com/laozhanghahaha/p/12374027.html">https://www.cnblogs.com/laozhanghahaha/p/12374027.html</a></p>
<p>假设一个二分类问题，样本有正负两个类别。那么模型预测的结果和真实标签的组合就有4种：<strong>TP，FP，FN，TN</strong>，如下图所示。这4个分别表示：实际为正样本你预测为正样本，实际为负样本你预测为正样本，实际为正样本你预测为负样本，实际为负样本你预测为负样本。<br><img src="https://img-blog.csdnimg.cn/20181214093105758.png" alt="在这里插入图片描述"></p>
<p><strong>TP(True Positive)</strong> －  被正确预测的正例．表示真实值为正，同时也被正确的预测为正；</p>
<p><strong>TN(True Negative)</strong> －被正确预测的反例．表示真实值为反例，也被正确的预测为反例；</p>
<p><strong>FP(False Positive)</strong> －  表示真实值为负例，被错误的预测为正例；</p>
<p><strong>FN(False Negative)－</strong>表示真实值为正例，被错误的预测为反例；</p>
<p>那么Precision和Recall表示什么意思？一般Precision和Recall都是针对某个类而言的，比如正类别的Recall，负类别的Recall等。如果<strong>你是10分类，那么可以有1这个类别的Precision，2这个类别的Precision，3这个类别的Recall等。而没有类似全部数据集的Recall或Precision这种说法。</strong></p>
<p>Precison,Recall都对应的2分类，是单独一个类别的准确率</p>
<p><img src="H:\myboke\mybike\source\images\precison.png" alt="image-20200322213614527"></p>
<p><strong>Precision</strong>适用场景:当反例被错误的预测为正例（假正例）产生的代价很高的时候，适合用查准率，因为高查准率意味着低假正率/假阳性．比如在垃圾邮件检测中，假正例意味着非垃圾邮件（实际为负）被错误的预测为垃圾邮件（预测为正）．如果一个垃圾邮件监测系统的查准率不高导致很多非垃圾邮件被归到垃圾邮箱里去，那么邮箱用户可能会丢失或者漏看一些很重要的邮件．</p>
<p><strong>Recall</strong>使用场景:当正例被错误的预测为反例（假反例）产生很高的代价时，用查全率，因为高查全率意味着低假反率/假阴性．比如说在银行的欺诈检测或医院的病患者检测中，如果将欺诈性交易（实际为正）预测为非欺诈性交易（预测为负），则可能会给银行带来非常严重的损失。再比如以最近的新冠疫情为例，如果一个患病者（实际为正）经过试剂检测被预测为没有患病（预测为负），这样的假反例或者说假阴性产生的风险就非常大．</p>
<p>还有一个概念：<strong>Accuracy，表示你有多少比例的样本预测对了</strong>，公式如下，分母永远是全部样本的数量，很好理解。很容易扩展到多类别的情况，比如10分类，<strong>那么分子就是第一个类别预测对了多少个+第二个类别预测对了多少个+…+第十个类别预测对了多少个。</strong></p>
<p><img src="https://img-blog.csdn.net/20170823081655165?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvdTAxNDM4MDE2NQ==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast" alt="这里写图片描述"></p>
<p>所以用Accuracy来进行判断分类的准确率</p>
<p>F1score的计算是这样的：1/F1score = 1/2(1/recall + 1/precision)*，简单换算后就成了：F1score=2recallprecision/(recall+precision)。同样F1score也是针对某个样本而言的。一般而言F1score用来综合precision和recall作为一个评价指标。还有F1score的变形，主要是添加一个权重系数可以根据需要对recall和precision赋予不同的权重。<br><img src="H:\myboke\mybike\source\images\F1SCORE.png" alt="image-20200322214139789"></p>

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